





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase :			Tuple     		=    {
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase :			List[str]     		=    [
        'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GroupViTModel',
        'GroupViTPreTrainedModel',
        'GroupViTTextModel',
        'GroupViTVisionModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase :			Optional[int]     		=    [
        'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFGroupViTModel',
        'TFGroupViTPreTrainedModel',
        'TFGroupViTTextModel',
        'TFGroupViTVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_groupvit import (
        GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        GroupViTConfig,
        GroupViTOnnxConfig,
        GroupViTTextConfig,
        GroupViTVisionConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_groupvit import (
            GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GroupViTModel,
            GroupViTPreTrainedModel,
            GroupViTTextModel,
            GroupViTVisionModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_groupvit import (
            TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFGroupViTModel,
            TFGroupViTPreTrainedModel,
            TFGroupViTTextModel,
            TFGroupViTVisionModel,
        )

else:
    import sys

    lowercase :			str     		=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


lowercase    :   Union[str, Any]						=			['bert-base-uncased', 'bert-base-cased']
lowercase    :   Any						=			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():




		class 					lowerCamelCase__				(							tf.keras.Model):




			'''simple docstring'''




			def __init__(   self						:List[Any]	,  a						:int		)						->					int:
										super().__init__()
										__UpperCamelCase      :  Any       =   tokenizer
										__UpperCamelCase      :  Union[str, Any]       =   AutoConfig.from_pretrained(a		)
										__UpperCamelCase      :  Dict       =   TFAutoModel.from_config(a		)







			def   _lowerCamelCase							(   self						:List[Any]	,  a						:Dict		)						->					Any:
										__UpperCamelCase      :  int       =   self.tokenizer(a		)
										__UpperCamelCase      :  int       =   self.bert(**a		)
										return out["pooler_output"]




@require_tf
@require_tensorflow_text
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:str		)						->					str:
								super().setUp()

								__UpperCamelCase      :  Dict       =   [
								    BertTokenizer.from_pretrained(a		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								__UpperCamelCase      :  List[Any]       =   [TFBertTokenizer.from_pretrained(a		) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(a	,  use_fast_bert_tokenizer=a		)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers		) == len(self.tf_tokenizers		)

								__UpperCamelCase      :  Dict       =   [
								    "This is a straightforward English test sentence.",
								    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
								    "Now we're going to add some Chinese: 一 二 三 一二三",
								    "And some much more rare Chinese: 齉 堃 齉堃",
								    "Je vais aussi écrire en français pour tester les accents",
								    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
								]
								__UpperCamelCase      :  Dict       =   list(zip(self.test_sentences	,  self.test_sentences[::-1]		)		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					int:
								for tokenizer, tf_tokenizer in zip(self.tokenizers	,  self.tf_tokenizers		):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  str       =   tokenizer(a	,  return_tensors="tf"	,  padding="longest"		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,  tf.intaa		) == tf_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Any:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  List[str]       =   tf_tokenizer(self.paired_sentences		)
															__UpperCamelCase      :  Union[str, Any]       =   tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]	,  text_pair=[sentence[1] for sentence in self.paired_sentences]	,  )
															for key in merged_outputs.keys():
																						self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,  tf.intaa		) == separated_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Any		)						->					Optional[int]:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  Optional[Any]       =   tf.function(a		)
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  Tuple       =   tf.constant(a		)
																						__UpperCamelCase      :  List[str]       =   compiled_tokenizer(a		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Tuple:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  str       =   ModelToSave(tokenizer=a		)
															__UpperCamelCase      :  List[Any]       =   tf.convert_to_tensor(self.test_sentences		)
															__UpperCamelCase      :  Optional[int]       =   model(a		)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																						__UpperCamelCase      :  Optional[Any]       =   Path(a		) / "saved.model"
																						model.save(a		)
																						__UpperCamelCase      :  List[Any]       =   tf.keras.models.load_model(a		)
															__UpperCamelCase      :  Optional[Any]       =   loaded_model(a		)
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output		)		)	,  1E-5		)